Krawtchouk moments as a new set of discrete orthogonal moments for image reconstruction

Yap, Pew-Thian; Raveendran, P.; Ong, S. H.

doi:10.1109/ijcnn.2002.1005595

Cited by 23 publications

(22 citation statements)

References 5 publications

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“…Within this class, Legendre and Zernike moments have been extensively researched in recent years [3][4][5][6]. However, these continuous orthogonal moments are associated with some major problems listed below [7][8][9]:…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, to remedy the abovementioned problems, sets of discrete orthogonal moment functions based on discrete basis sets such as Tchebichef polynomials [7] and Krawtchouk polynomials [8] [9] were introduced. The implementation of these discrete orthogonal moments doesn't involve any numerical approximation or coordinate space transformation since the basis set is orthogonal within the discrete domain of the image coordinate space.…”

Section: Introductionmentioning

confidence: 99%

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Combining Krawtchouk Moments and HMMs for Offline Handwritten Chinese Character Recognition

Wang¹,

Xie²,

Yang

2006

2006 3rd International IEEE Conference Intelligent Systems

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This paper proposes an approach combining the set of Krawtchouk moments and HMMs (Hidden Markov Models) for unconstrained offline handwritten Chinese character recognition. The Krawtchouk moments can be effectively used as pattern features in analysis of twodimensional images. Since the basis set is orthogonal in the discrete domain of the image coordinate space, the implementation of Krawtchouk moments doesn't involve any numerical approximation or coordinate transformation. This property makes Krawtchouk moments superior to conventional, more continuous moments such as Legendre moments and Zernike moments. In the field of character recognition, HMMs architecture has been used widely because of its strong adaptability to written distortion and variability. In this paper, Krawtchouk moments and HMMs are combined to recognize unconstrained offline handwritten Chinese amount in words. Experiments show that the average recognition of Krawtchouk is higher than Zernike and Legendre moments within the HMM framework.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Krawtchouk Moments and HMMs for Offline Handwritten Chinese Character Recognition

Wang¹,

Xie²,

Yang

2006

2006 3rd International IEEE Conference Intelligent Systems

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show abstract

“…Parameters are generally classified into two main categories: global and local [1]. For parameter feature extraction purpose, we use Krawtchouk Moments [10], [11], [12]. It extract optimal feature from the signature sample depending on the Maximum Entropy Principle, which reduces the input dimensionality of feature vector by eliminating some features with low specified criteria.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Krawtchouk moments are set of moments formed by using krawtchouk polynomials [11] [13] and are a set of polynomials associated with the binomial distribution. The set of moments are rotation, scale and translation invariant.…”

Section: Feature Extractionmentioning

confidence: 99%

“…It extract optimal feature from the signature sample depending on the Maximum Entropy Principle, which reduces the input dimensionality of feature vector by eliminating some features with low specified criteria. Krawtchouk moment invariant [10], [11] have desirable properties of being invariant under image scaling, translation and rotation.…”

Section: Feature Extractionmentioning

confidence: 99%

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Offline Signature Verification using FMCN with GA based Optimization of Features

Jain¹,

Patil²

2012

IJCA

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In recent years, along with extraordinary diffusion of internet and growing need of personal identification in many applications, signature verification is considered with interest. This paper proposed an offline signature verification method based on Genetic Algorithm and Fuzzy Min Max Neural Network Classifier with Compensatory Neuron. The proposed method is basically consists of two steps. At first step optimizing the features using genetic algorithm, and at second step signature recognition is done using Fuzzy Min Max Neural Network Classifier with Compensatory Neurons. The sample of signatures is used to represent a particular person. The sample signature is first preprocessed, and then features of the processed signature are extracted by using Krawtchouk moment. After feature extraction, these features are optimized by using genetic algorithm and finally optimized features are given to the classification phase for recognition. With this proposed method, we get the 98% accuracy in recognition and less time is required for classification with optimized features as compared to time required for classification without optimizing feature.

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Fast Recursive Computation of Krawtchouk Polynomials

et al. 2017

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Krawtchouk polynomials (KPs) and their moments are used widely in the field of signal processing for their superior discriminatory properties. This study proposes a new fast recursive algorithm to compute Krawtchouk polynomial coefficients (KPCs). This algorithm is based on the symmetry property of KPCs along the primary and secondary diagonals of the polynomial array. The n−x plane of the KP array is partitioned into four triangles, which are symmetrical across the primary and secondary diagonals. The proposed algorithm computes the KPCs for only one triangle (partition), while the coefficients of the other three triangles (partitions) can be computed using the derived symmetry properties of the KP. Therefore, only N / 4 recursion times are required. The proposed algorithm can also be used to compute polynomial coefficients for different values of the parameter p in interval (0, 1). The performance of the proposed algorithm is compared with that in previous literature in terms of image reconstruction error, polynomial size, and computation cost. Moreover, the proposed algorithm is applied in a face recognition system to determine the impact of parameter p on feature extraction ability. Simulation results show that the proposed algorithm has a remarkable advantage over other existing algorithms for a wide range of parameters p and polynomial size N, especially in reducing the computation time and the number of operations utilized.

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Krawtchouk moments as a new set of discrete orthogonal moments for image reconstruction

Cited by 23 publications

References 5 publications

Combining Krawtchouk Moments and HMMs for Offline Handwritten Chinese Character Recognition

Combining Krawtchouk Moments and HMMs for Offline Handwritten Chinese Character Recognition

Offline Signature Verification using FMCN with GA based Optimization of Features

Fast Recursive Computation of Krawtchouk Polynomials

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